Jared Adolf‐Bryfogle

EGGNet is a geometric deep learning framework for predicting molecule-protein interactions, achieving competitive performance on binding affinity and interface prediction tasks.

The paper presents a framework for ensuring reproducibility in bio-macromolecular modeling through automated benchmarks and a test server, demonstrating its utility for Rosetta and other tools.

This paper expands the Rosetta software suite with tools for glycan modeling, density fitting, and rational sequon design, achieving high accuracy in predicting and building glycan structures.

The paper analyzes the structure and sequence of the antibody DE loop, revealing its role in antigen binding and its variability in HIV-1 antibodies.